Cybersecurity in implantable medical devices

Mención Internacional en el título de doctorImplantable Medical Devices (IMDs) are electronic devices implanted within the body to treat a medical condition, monitor the state or improve the functioning of some body part, or just to provide the patient with a capability that he did not possess before [86]. Current examples of IMDs include pacemakers and defibrillators to monitor and treat cardiac conditions; neurostimulators for deep brain stimulation in cases such as epilepsy or Parkinson; drug delivery systems in the form of infusion pumps; and a variety of biosensors to acquire and process different biosignals. Some of the newest IMDs have started to incorporate numerous communication and networking functions—usually known as “telemetry”—, as well as increasingly more sophisticated computing capabilities. This has provided implants with more intelligence and patients with more autonomy, as medical personnel can access data and reconfigure the implant remotely (i.e., without the patient being physically present in medical facilities). Apart from a significant cost reduction, telemetry and computing capabilities also allow healthcare providers to constantly monitor the patient’s condition and to develop new diagnostic techniques based on an Intra Body Network (IBN) of medical devices [25, 26, 201]. Evolving from a mere electromechanical IMD to one with more advanced computing and communication capabilities has many benefits but also entails numerous security and privacy risks for the patient. The majority of such risks are relatively well known in classical computing scenarios, though in many respects their repercussions are far more critical in the case of implants. Attacks against an IMD can put at risk the safety of the patient who carries it, with fatal consequences in certain cases. Causing an intentional malfunction of an implant can lead to death and, as recognized by the U.S. Food and Drug Administration (FDA), such deliberate attacks could be far more difficult to detect than accidental ones [61]. Furthermore, these devices store and transmit very sensitive medical information that requires protection, as dictated by European (e.g., Directive 95/46/ECC) and U.S. (e.g., CFR 164.312) Directives [94, 204]. The wireless communication capabilities present in many modern IMDs are a major source of security risks, particularly while the patient is in open (i.e., non-medical) environments. To begin with, the implant becomes no longer “invisible”, as its presence could be remotely detected [48]. Furthermore, it facilitates the access to transmitted data by eavesdroppers who simply listen to the (insecure) channel [83]. This could result in a major privacy breach, as IMDs store sensitive information such as vital signals, diagnosed conditions, therapies, and a variety of personal data (e.g., birth date, name, and other medically relevant identifiers). A vulnerable communication channel also makes it easier to attack the implant in ways similar to those used against more common computing devices [118, 129, 156], i.e., by forging, altering, or replying previously captured messages [82]. This could potentially allow an adversary to monitor and modify the implant without necessarily being close to the victim [164]. In this regard, the concerns of former U.S. vice-president Dick Cheney constitute an excellent example: he had his Implantable Cardioverter Defibrillator (ICD) replaced by another without WiFi capability [219]. While there are still no known real-world incidents, several attacks on IMDs have been successfully demonstrated in the lab [83, 133, 143]. These attacks have shown how an adversary can disable or reprogram therapies on an ICD with wireless connectivity, and even inducing a shock state to the patient [65]. Other attacks deplete the battery and render the device inoperative [91], which often implies that the patient must undergo a surgical procedure to have the IMD replaced. Moreover, in the case of cardiac implants, they have a switch that can be turned off merely by applying a magnetic field [149]. The existence of this mechanism is motivated by the need to shield ICDs to electromagnetic fields, for instance when the patient undergoes cardiac surgery using electrocautery devices [47]. However, this could be easily exploited by an attacker, since activating such a primitive mechanism does not require any kind of authentication. In order to prevent attacks, it is imperative that the new generation of IMDs will be equipped with strong mechanisms guaranteeing basic security properties such as confidentiality, integrity, and availability. For example, mutual authentication between the IMD and medical personnel is essential, as both parties must be confident that the other end is who claims to be. In the case of the IMD, only commands coming from authenticated parties should be considered, while medical personnel should not trust any message claiming to come from the IMD unless sufficient guarantees are given. Preserving the confidentiality of the information stored in and transmitted by the IMD is another mandatory aspect. The device must implement appropriate security policies that restrict what entities can reconfigure the IMD or get access to the information stored in it, ensuring that only authorized operations are executed. Similarly, security mechanisms have to be implemented to protect the content of messages exchanged through an insecure wireless channel. Integrity protection is equally important to ensure that information has not been modified in transit. For example, if the information sent by the implant to the Programmer is altered, the doctor might make a wrong decision. Conversely, if a command sent to the implant is forged, modified, or simply contains errors, its execution could result in a compromise of the patient’s physical integrity. Technical security mechanisms should be incorporated in the design phase and complemented with appropriate legal and administrative measures. Current legislation is rather permissive in this regard, allowing the use of implants like ICDs that do not incorporate any security mechanisms. Regulatory authorities like the FDA in the U.S or the EMA (European Medicines Agency) in Europe should promote metrics and frameworks for assessing the security of IMDs. These assessments should be mandatory by law, requiring an adequate security level for an implant before approving its use. Moreover, both the security measures supported on each IMD and the security assessment results should be made public. Prudent engineering practices well known in the safety and security domains should be followed in the design of IMDs. If hardware errors are detected, it often entails a replacement of the implant, with the associated risks linked to a surgery. One of the main sources of failure when treating or monitoring a patient is precisely malfunctions of the device itself. These failures are known as “recalls” or “advisories”, and it is estimated that they affect around 2.6% of patients carrying an implant. Furthermore, the software running on the device should strictly support the functionalities required to perform the medical and operational tasks for what it was designed, and no more [66, 134, 213]. In Chapter 1, we present a survey of security and privacy issues in IMDs, discuss the most relevant mechanisms proposed to address these challenges, and analyze their suitability, advantages, and main drawbacks. In Chapter 2, we show how the use of highly compressed electrocardiogram (ECG) signals (only 24 coefficients of Hadamard Transform) is enough to unequivocally identify individuals with a high performance (classification accuracy of 97% and with identification system errors in the order of 10−2). In Chapter 3 we introduce a new Continuous Authentication scheme that, contrarily to previous works in this area, considers ECG signals as continuous data streams. The proposed ECG-based CA system is intended for real-time applications and is able to offer an accuracy up to 96%, with an almost perfect system performance (kappa statistic > 80%). In Chapter 4, we propose a distance bounding protocol to manage access control of IMDs: ACIMD. ACIMD combines two features namely identity verification (authentication) and proximity verification (distance checking). The authentication mechanism we developed conforms to the ISO/IEC 9798-2 standard and is performed using the whole ECG signal of a device holder, which is hardly replicable by a distant attacker. We evaluate the performance of ACIMD using ECG signals of 199 individuals over 24 hours, considering three adversary strategies. Results show that an accuracy of 87.07% in authentication can be achieved. Finally, in Chapter 5 we extract some conclusions and summarize the published works (i.e., scientific journals with high impact factor and prestigious international conferences).Los Dispositivos Médicos Implantables (DMIs) son dispositivos electrónicos implantados dentro del cuerpo para tratar una enfermedad, controlar el estado o mejorar el funcionamiento de alguna parte del cuerpo, o simplemente para proporcionar al paciente una capacidad que no poseía antes [86]. Ejemplos actuales de DMI incluyen marcapasos y desfibriladores para monitorear y tratar afecciones cardíacas; neuroestimuladores para la estimulación cerebral profunda en casos como la epilepsia o el Parkinson; sistemas de administración de fármacos en forma de bombas de infusión; y una variedad de biosensores para adquirir y procesar diferentes bioseñales. Los DMIs más modernos han comenzado a incorporar numerosas funciones de comunicación y redes (generalmente conocidas como telemetría) así como capacidades de computación cada vez más sofisticadas. Esto ha propiciado implantes con mayor inteligencia y pacientes con más autonomía, ya que el personal médico puede acceder a los datos y reconfigurar el implante de forma remota (es decir, sin que el paciente esté físicamente presente en las instalaciones médicas). Aparte de una importante reducción de costos, las capacidades de telemetría y cómputo también permiten a los profesionales de la atención médica monitorear constantemente la condición del paciente y desarrollar nuevas técnicas de diagnóstico basadas en una Intra Body Network (IBN) de dispositivos médicos [25, 26, 201]. Evolucionar desde un DMI electromecánico a uno con capacidades de cómputo y de comunicación más avanzadas tiene muchos beneficios pero también conlleva numerosos riesgos de seguridad y privacidad para el paciente. La mayoría de estos riesgos son relativamente bien conocidos en los escenarios clásicos de comunicaciones entre dispositivos, aunque en muchos aspectos sus repercusiones son mucho más críticas en el caso de los implantes. Los ataques contra un DMI pueden poner en riesgo la seguridad del paciente que lo porta, con consecuencias fatales en ciertos casos. Causar un mal funcionamiento intencionado en un implante puede causar la muerte y, tal como lo reconoce la Food and Drug Administration (FDA) de EE.UU, tales ataques deliberados podrían ser mucho más difíciles de detectar que los ataques accidentales [61]. Además, estos dispositivos almacenan y transmiten información médica muy delicada que requiere se protegida, según lo dictado por las directivas europeas (por ejemplo, la Directiva 95/46/ECC) y estadunidenses (por ejemplo, la Directiva CFR 164.312) [94, 204]. Si bien todavía no se conocen incidentes reales, se han demostrado con éxito varios ataques contra DMIs en el laboratorio [83, 133, 143]. Estos ataques han demostrado cómo un adversario puede desactivar o reprogramar terapias en un marcapasos con conectividad inalámbrica e incluso inducir un estado de shock al paciente [65]. Otros ataques agotan la batería y dejan al dispositivo inoperativo [91], lo que a menudo implica que el paciente deba someterse a un procedimiento quirúrgico para reemplazar la batería del DMI. Además, en el caso de los implantes cardíacos, tienen un interruptor cuya posición de desconexión se consigue simplemente aplicando un campo magnético intenso [149]. La existencia de este mecanismo está motivada por la necesidad de proteger a los DMIs frete a posibles campos electromagnéticos, por ejemplo, cuando el paciente se somete a una cirugía cardíaca usando dispositivos de electrocauterización [47]. Sin embargo, esto podría ser explotado fácilmente por un atacante, ya que la activación de dicho mecanismo primitivo no requiere ningún tipo de autenticación. Garantizar la confidencialidad de la información almacenada y transmitida por el DMI es otro aspecto obligatorio. El dispositivo debe implementar políticas de seguridad apropiadas que restrinjan qué entidades pueden reconfigurar el DMI o acceder a la información almacenada en él, asegurando que sólo se ejecuten las operaciones autorizadas. De la misma manera, mecanismos de seguridad deben ser implementados para proteger el contenido de los mensajes intercambiados a través de un canal inalámbrico no seguro. La protección de la integridad es igualmente importante para garantizar que la información no se haya modificado durante el tránsito. Por ejemplo, si la información enviada por el implante al programador se altera, el médico podría tomar una decisión equivocada. Por el contrario, si un comando enviado al implante se falsifica, modifica o simplemente contiene errores, su ejecución podría comprometer la integridad física del paciente. Los mecanismos de seguridad deberían incorporarse en la fase de diseño y complementarse con medidas legales y administrativas apropiadas. La legislación actual es bastante permisiva a este respecto, lo que permite el uso de implantes como marcapasos que no incorporen ningún mecanismo de seguridad. Las autoridades reguladoras como la FDA en los Estados Unidos o la EMA (Agencia Europea de Medicamentos) en Europa deberían promover métricas y marcos para evaluar la seguridad de los DMIs. Estas evaluaciones deberían ser obligatorias por ley, requiriendo un nivel de seguridad adecuado para un implante antes de aprobar su uso. Además, tanto las medidas de seguridad implementadas en cada DMI como los resultados de la evaluación de su seguridad deberían hacerse públicos. Buenas prácticas de ingeniería en los dominios de la protección y la seguridad deberían seguirse en el diseño de los DMIs. Si se detectan errores de hardware, a menudo esto implica un reemplazo del implante, con los riesgos asociados y vinculados a una cirugía. Una de las principales fuentes de fallo al tratar o monitorear a un paciente es precisamente el mal funcionamiento del dispositivo. Estos fallos se conocen como “retiradas”, y se estima que afectan a aproximadamente el 2,6 % de los pacientes que llevan un implante. Además, el software que se ejecuta en el dispositivo debe soportar estrictamente las funcionalidades requeridas para realizar las tareas médicas y operativas para las que fue diseñado, y no más [66, 134, 213]. En el Capítulo 1, presentamos un estado de la cuestión sobre cuestiones de seguridad y privacidad en DMIs, discutimos los mecanismos más relevantes propuestos para abordar estos desafíos y analizamos su idoneidad, ventajas y principales inconvenientes. En el Capítulo 2, mostramos cómo el uso de señales electrocardiográficas (ECGs) altamente comprimidas (sólo 24 coeficientes de la Transformada Hadamard) es suficiente para identificar inequívocamente individuos con un alto rendimiento (precisión de clasificación del 97% y errores del sistema de identificación del orden de 10−2). En el Capítulo 3 presentamos un nuevo esquema de Autenticación Continua (AC) que, contrariamente a los trabajos previos en esta área, considera las señales ECG como flujos de datos continuos. El sistema propuesto de AC basado en señales cardíacas está diseñado para aplicaciones en tiempo real y puede ofrecer una precisión de hasta el 96%, con un rendimiento del sistema casi perfecto (estadístico kappa > 80 %). En el Capítulo 4, proponemos un protocolo de verificación de la distancia para gestionar el control de acceso al DMI: ACIMD. ACIMD combina dos características, verificación de identidad (autenticación) y verificación de la proximidad (comprobación de la distancia). El mecanismo de autenticación es compatible con el estándar ISO/IEC 9798-2 y se realiza utilizando la señal ECG con todas sus ondas, lo cual es difícilmente replicable por un atacante que se encuentre distante. Hemos evaluado el rendimiento de ACIMD usando señales ECG de 199 individuos durante 24 horas, y hemos considerando tres estrategias posibles para el adversario. Los resultados muestran que se puede lograr una precisión del 87.07% en la au tenticación. Finalmente, en el Capítulo 5 extraemos algunas conclusiones y resumimos los trabajos publicados (es decir, revistas científicas con alto factor de impacto y conferencias internacionales prestigiosas).Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Arturo Ribagorda Garnacho.- Secretario: Jorge Blasco Alís.- Vocal: Jesús García López de Lacall

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

ECG classification and prognostic approach towards personalized healthcare

A very important aspect of personalized healthcare is to continuously monitor an individual’s health using wearable biomedical devices and essentially to analyse and if possible to predict potential health hazards that may prove fatal if not treated in time. The prediction aspect embedded in the system helps in avoiding delays in providing timely medical treatment, even before an individual reaches a critical condition. Despite of the availability of modern wearable health monitoring devices, the real-time analyses and prediction component seems to be missing with these devices. The research work illustrated in this paper, at an outset, focussed on constantly monitoring an individual's ECG readings using a wearable 3-lead ECG kit and more importantly focussed on performing real-time analyses to detect arrhythmia to be able to identify and predict heart risk. Also, current research shows extensive use of heart rate variability (HRV) analysis and machine learning for arrhythmia classification, which however depends on the morphology of the ECG waveforms and the sensitivity of the ECG equipment. Since a wearable 3-lead ECG kit was used, the accuracy of classification had to be dealt with at the machine learning phase, so a unique feature extraction method was developed to increase the accuracy of classification. As a case study a very widely used Arrhythmia database (MIT-BIH, Physionet) was used to develop learning, classification and prediction models. Neuralnet fitting models on the extracted features showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. Current experiments show 99.4% accuracy using k-NN Classification models and show values of Cross-Entropy Error of 7.6 and misclassification error value of 1.2 on test data using scaled conjugate gradient pattern matching algorithms. Software components were developed for wearable devices that took ECG readings from a 3-Lead ECG data acquisition kit in real time, de-noised, filtered and relayed the sample readings to the tele health analytical server. The analytical server performed the classification and prediction tasks based on the trained classification models and could raise appropriate alarms if ECG abnormalities of V (Premature Ventricular Contraction: PVC), A (Atrial Premature Beat: APB), L (Left bundle branch block beat), R (Right bundle branch block beat) type annotations in MITDB were detected. The instruments were networked using IoT (Internet of Things) devices and abnormal ECG events related to arrhythmia, from analytical server could be logged using an FHIR web service implementation, according to a SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to take appropriate and timely decisions. The system focused on ‘preventive care rather than remedial cure’ which has become a major focus of all the health care and cure institutions across the globe

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data